Evaluation of the Performance of the Integration of Remote Sensing and Noah Hydrologic Model for Soil Moisture Estimation in Hetao Irrigation Region of Inner Mongolia
As an important parameter in Land surface system research, surface soil moisture (SSM) links the surface water and groundwater that plays a key role in water resources, agricultural management and global warming studies. Remote sensing techniques provide a direct and convenient means to estimate SSM...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2020-09-01
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| Series: | Canadian Journal of Remote Sensing |
| Online Access: | http://dx.doi.org/10.1080/07038992.2020.1810003 |
| _version_ | 1797661147293286400 |
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| author | Dianjun Zhang Jie Zhan Zhi Qiao Robert Župan |
| author_facet | Dianjun Zhang Jie Zhan Zhi Qiao Robert Župan |
| author_sort | Dianjun Zhang |
| collection | DOAJ |
| description | As an important parameter in Land surface system research, surface soil moisture (SSM) links the surface water and groundwater that plays a key role in water resources, agricultural management and global warming studies. Remote sensing techniques provide a direct and convenient means to estimate SSM on a regional scale. In this study, the performance of the normalized land surface temperature-vegetation index (LST-VI) model was evaluated using the in situ soil moisture measurements at Hetao irrigation region of Inner Mongolia that is a representative semi-arid area with relatively uniform underlying surface. The model was used to estimate soil moisture from HJ-1B and Landsat 8 images on clear days in 2014–2017. The overall SSM estimation accuracy was high, and the average RMSE was approximately 0.04 m3/m3. Moreover, a systematic sensitivity analysis was conducted for the input parameters and other impact factors. The results demonstrated that the model could credibly monitor the regional surface soil water content. |
| first_indexed | 2024-03-11T18:40:41Z |
| format | Article |
| id | doaj.art-d8415317f42e40858a06b8f01372ed92 |
| institution | Directory Open Access Journal |
| issn | 1712-7971 |
| language | English |
| last_indexed | 2024-03-11T18:40:41Z |
| publishDate | 2020-09-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Canadian Journal of Remote Sensing |
| spelling | doaj.art-d8415317f42e40858a06b8f01372ed922023-10-12T13:36:23ZengTaylor & Francis GroupCanadian Journal of Remote Sensing1712-79712020-09-0146555256610.1080/07038992.2020.18100031810003Evaluation of the Performance of the Integration of Remote Sensing and Noah Hydrologic Model for Soil Moisture Estimation in Hetao Irrigation Region of Inner MongoliaDianjun Zhang0Jie Zhan1Zhi Qiao2Robert Župan3School of Marine Science and Technology, Tianjin UniversitySchool of Marine Science and Technology, Tianjin UniversityKey Laboratory of Indoor Air Environment Quality Control, School of Environmental Science and Engineering, Tianjin UniversityGeodesy, University of ZagrebAs an important parameter in Land surface system research, surface soil moisture (SSM) links the surface water and groundwater that plays a key role in water resources, agricultural management and global warming studies. Remote sensing techniques provide a direct and convenient means to estimate SSM on a regional scale. In this study, the performance of the normalized land surface temperature-vegetation index (LST-VI) model was evaluated using the in situ soil moisture measurements at Hetao irrigation region of Inner Mongolia that is a representative semi-arid area with relatively uniform underlying surface. The model was used to estimate soil moisture from HJ-1B and Landsat 8 images on clear days in 2014–2017. The overall SSM estimation accuracy was high, and the average RMSE was approximately 0.04 m3/m3. Moreover, a systematic sensitivity analysis was conducted for the input parameters and other impact factors. The results demonstrated that the model could credibly monitor the regional surface soil water content.http://dx.doi.org/10.1080/07038992.2020.1810003 |
| spellingShingle | Dianjun Zhang Jie Zhan Zhi Qiao Robert Župan Evaluation of the Performance of the Integration of Remote Sensing and Noah Hydrologic Model for Soil Moisture Estimation in Hetao Irrigation Region of Inner Mongolia Canadian Journal of Remote Sensing |
| title | Evaluation of the Performance of the Integration of Remote Sensing and Noah Hydrologic Model for Soil Moisture Estimation in Hetao Irrigation Region of Inner Mongolia |
| title_full | Evaluation of the Performance of the Integration of Remote Sensing and Noah Hydrologic Model for Soil Moisture Estimation in Hetao Irrigation Region of Inner Mongolia |
| title_fullStr | Evaluation of the Performance of the Integration of Remote Sensing and Noah Hydrologic Model for Soil Moisture Estimation in Hetao Irrigation Region of Inner Mongolia |
| title_full_unstemmed | Evaluation of the Performance of the Integration of Remote Sensing and Noah Hydrologic Model for Soil Moisture Estimation in Hetao Irrigation Region of Inner Mongolia |
| title_short | Evaluation of the Performance of the Integration of Remote Sensing and Noah Hydrologic Model for Soil Moisture Estimation in Hetao Irrigation Region of Inner Mongolia |
| title_sort | evaluation of the performance of the integration of remote sensing and noah hydrologic model for soil moisture estimation in hetao irrigation region of inner mongolia |
| url | http://dx.doi.org/10.1080/07038992.2020.1810003 |
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